Particle Cloud

Using the same setup as detailed_look.jl or example2(), here we simulate a point cloud getting advected by the flow field. For additional documentation e.g. see : 1, 2, 3, 4

1. Import Software

using IndividualDisplacements, OrdinaryDiffEq, Statistics
p=dirname(pathof(IndividualDisplacements))
include(joinpath(p,"../examples/recipes_plots.jl"))
include(joinpath(p,"../examples/example123.jl"));

2. Setup Problem

𝑃,Γ=example2_setup()

ii1=5:5:40; ii2=5:5:25
x=vec([x-0.5 for x in ii1, y in ii2])
y=vec([y-0.5 for x in ii1, y in ii2])
xy = permutedims([[x[i];y[i];1.0] for i in eachindex(x)])

solv(prob) = solve(prob,Tsit5(),reltol=1e-6,abstol=1e-6)
tr = DataFrame(ID=Int[], x=Float64[], y=Float64[], t=Float64[])

#𝐼 = Individuals{Float64,2}(📌=xy[:,:], 🔴=tr, 🆔=collect(1:size(xy,2)),

                    🚄 = dxdt!, ∫ = solv, 🔧 = postprocess_xy, 𝑃=𝑃);

I=(position=xy,record=deepcopy(tr),velocity=dxdt!,
   integration=solv,postprocessing=postprocess_xy,parameters=𝑃)
𝐼=Individuals(I)

  📌 details     = (1, 40) Array{Float64,1}
  🔴 details     = (0, 4) ["ID", "x", "y", "t"]
  🆔 range       = (1, 40)
  🚄 function    = dxdt!
  ∫  function    = solv
  🔧 function    = postprocess_xy
  𝑃  details     = (:u0, :u1, :v0, :v1, :𝑇)

3. Compute Trajectories

𝑇 = (0.0,2998*3600.0)
∫!(𝐼,𝑇)

1×40 Array{Array{Float64,1},2}:
 [6.2601, -11.4048, 1.0]  [10.9042, 123.049, 1.0]  …  [29.8254, 21.0132, 1.0]

4. Display results

𝐼.🔴.lon=5000*𝐼.🔴.x
𝐼.🔴.lat=5000*𝐼.🔴.y
plt=plot_paths(𝐼.🔴,size(xy,2),100000.0)

Compare with trajectory output from MITgcm

#df=read_flt(IndividualDisplacements.flt_example,Float32)
#ref=plot_paths(df,size(xy,2),100000.0)

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